DESCRIPTION (APPLICATION ABSTRACT): Recent efforts to reduce costs and streamline the delivery of healthcare have led to significant changes in the acute care workplace. In addition, the increased use of technology and an aging population are likely to lead to an increased volume of intensive care unit (ICU) patients in the future. The main objective of this study is to investigate the effect of varied working conditions (workforce staffing and organizational climate) in ICUs on elderly patient safety outcomes (nosocomial infections [NIs], length of stay, mortality, and disposition at discharge), and healthcare worker safety (musculoskeletal injuries, blood/body fluid exposure, sick days, and disability days). To do this, we will obtain and analyze data from a variety of sources. We propose to 1) obtain Medicare data for patients admitted to ICUs participating in the National Nosocomial Infection Surveillance (NNIS) system during 3 different years (1996, 1999, and 2002); 2) link the Medicare data to NNIS data and other existing datasets; 3) survey the same hospitals regarding ICU specific workforce staffing and healthcare worker safety; and 4) survey nursing personnel currently employed in these ICUs regarding organizational climate. We will employ econometric multivariate regression data analytic methods in which we will control for patient severity of illness, the nurse labor market and healthcare setting characteristics. A unique aspect of our proposed study is the use of NNIS data (the gold standard) to measure an important nurse-sensitive patient safety outcome across hospital-linked claims data and working conditions. By examining these data, we will be able to assess the impact of the changes in ICU working conditions on patient safety outcomes, and healthcare worker safety. Because ICU care and the nursing workforce are expected to continue to experience rapid change, this information is of major importance to those who manage or oversee health care organizations and set policies affecting the working conditions. In addition, obtaining NNIS and Medicare data creates an opportunity to explore the sensitivity and specificity of identifying NI through claims data.